Zhan Ye
Zhan Ye
Senior Biostatistician
Verified email at pfizer.com
Cited by
Cited by
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
Z Su, PP Łabaj, S Li, J Thierry-Mieg, D Thierry-Mieg, W Shi, C Wang, ...
Nature biotechnology 32 (9), 903-914, 2014
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
Genome Biology 16, 2015
Opportunities for drug repositioning from phenome-wide association studies
M Rastegar-Mojarad, Z Ye, JM Kolesar, SJ Hebbring, SM Lin
Nature biotechnology 33 (4), 342-345, 2015
A PheWAS approach in studying HLA-DRB1* 1501
SJ Hebbring, SJ Schrodi, Z Ye, Z Zhou, D Page, MH Brilliant
Genes & Immunity 14 (3), 187-191, 2013
Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures
SA Munro, SP Lund, PS Pine, H Binder, DA Clevert, A Conesa, J Dopazo, ...
Nature communications 5 (1), 1-10, 2014
Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants
JH Karnes, L Bastarache, CM Shaffer, S Gaudieri, Y Xu, AM Glazer, ...
Science Translational Medicine 9 (389), 2017
Genetic-based prediction of disease traits: prediction is very difficult, especially about the future
SJ Schrodi, S Mukherjee, Y Shan, G Tromp, JJ Sninsky, AP Callear, ...
Frontiers in genetics 5, 162, 2014
Collecting and analyzing patient experiences of health care from social media
M Rastegar-Mojarad, Z Ye, D Wall, N Murali, S Lin
JMIR research protocols 4 (3), e78, 2015
Phenome-wide association studies (PheWASs) for functional variants
Z Ye, J Mayer, L Ivacic, Z Zhou, M He, SJ Schrodi, D Page, MH Brilliant, ...
European Journal of Human Genetics 23 (4), 523-529, 2015
Application of clinical text data for phenome-wide association studies (PheWASs)
SJ Hebbring, M Rastegar-Mojarad, Z Ye, J Mayer, C Jacobson, S Lin
Bioinformatics 31 (12), 1981-1987, 2015
Genome wide association study of SNP-, gene-, and pathway-based approaches to identify genes influencing susceptibility to Staphylococcus aureus infections
Z Ye, DA Vasco, TC Carter, MH Brilliant, SJ Schrodi, SK Shukla
Frontiers in genetics 5, 125, 2014
Sparktext: Biomedical text mining on big data framework
Z Ye, AP Tafti, KY He, K Wang, MM He
PloS one 11 (9), e0162721, 2016
Adverse drug event discovery using biomedical literature: a big data neural network adventure
AP Tafti, J Badger, E LaRose, E Shirzadi, A Mahnke, J Mayer, Z Ye, ...
JMIR medical informatics 5 (4), e51, 2017
Phenome-wide association study maps new diseases to the human major histocompatibility complex region
J Liu, Z Ye, JG Mayer, BA Hoch, C Green, L Rolak, C Cold, SS Khor, ...
Journal of medical genetics 53 (10), 681-689, 2016
Medical care providers’ perspectives on dental information needs in electronic health records
A Acharya, N Shimpi, A Mahnke, R Mathias, Z Ye
The Journal of the American Dental Association 148 (5), 328-337, 2017
SeqHBase: a big data toolset for family based sequencing data analysis
M He, TN Person, SJ Hebbring, E Heinzen, Z Ye, SJ Schrodi, ...
Journal of medical genetics 52 (4), 282-288, 2015
The nasal microbiota of dairy farmers is more complex than oral microbiota, reflects occupational exposure, and provides competition for staphylococci
SK Shukla, Z Ye, S Sandberg, I Reyes, TR Fritsche, M Keifer
PloS one 12 (8), e0183898, 2017
Rodriguez syndrome with SF3B4 mutation: A severe form of Nager syndrome?
E McPherson, C Zaleski, Z Ye, S Lin
American journal of medical genetics Part A 164 (7), 1841-1845, 2014
Use of an electronic medical record to create the marshfield clinic twin/multiple birth cohort
J Mayer, T Kitchner, Z Ye, Z Zhou, M He, SJ Schrodi, SJ Hebbring
Genetic epidemiology 38 (8), 692-698, 2014
Identifying genetically driven clinical phenotypes using linear mixed models
JD Mosley, JS Witte, EK Larkin, L Bastarache, CM Shaffer, JH Karnes, ...
Nature communications 7 (1), 1-8, 2016
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